Creator · JetBrains
Last updated · Aug 24, 2026
importing-a-codebase
Use when the repo holds real source code but no specs: the existing-codebase branch of setting-up-a-project, normally reached via that dispatcher, directly only when the situation is unmistakable. Not for empty workspaces (starting-a-new-project) or feature work in a specced proj
Sandbox only
Install targets
Codex install prompt
Install the "importing-a-codebase" agent skill from https://github.com/JetBrains/thinkrail/tree/main/packages/pi-thinkrail-workflow/skills/importing-a-codebase. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use when the repo holds real source code but no specs: the existing-codebase branch of setting-up-a-project, normally reached via that dispatcher, directly only when the situation is unmistakable. Not for empty workspaces (starting-a-new-project) or feature work in a specced project (brainstorming). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"jetbrains-importing-a-codebase","task":"Install importing-a-codebase","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + Cursor + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add JetBrains/thinkrail --skill importing-a-codebase
Maintenance
fresh
Pushed today
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
38
63/100 Quality · 77/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · Low GitHub adoption signal
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Human review before install
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
38 GitHub stars
Repo activity
38 stars, 8 forks
Maintenance
Pushed today
License
Apache-2.0
Install
npx skills add JetBrains/thinkrail --skill importing-a-codebase
Install safety
standard package or runtime install path
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Review before production
- Financial research output is not financial advice; require human review before any live investment decision.
- Low GitHub adoption signal
- Quality score needs review
- GitHub adoption: 38 GitHub stars
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is declared
- No Agent Proven outcome evidence yet
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
View technical data+
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- GitHub automation workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Inspect repository metadata
Suited agents
Install decision
- Command
- npx skills add JetBrains/thinkrail --skill importing-a-codebase
- Policy
- review
- Human review
- yes
Trust and risk
- Trust
- 69/100
- Audit
- 79/100
- Risk level
- Needs review
Outcome loop
- Endpoint
- /api/agent/outcome
- Event ID
- resolve
- Outcomes
- 5
Install command
npx skills add JetBrains/thinkrail --skill importing-a-codebaseDo not use when
- teams that need a vendor-supported SLA
- production agents without a repository review
- Low GitHub adoption signal
- No OpenAgentSkill engagement data yet
- Financial research output is not financial advice; require human review before any live investment decision
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Agent safety v2
59/100 · Review before install
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Browser automation
Skill may drive a browser or interact with web pages.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Filesystem access
Skill may read or write project files, documents, generated artifacts, or local workspace state.
- Financial research output is not financial advice; require human review before any live investment decision
Agent resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20importing-a-codebase%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20importing-a-codebase%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/jetbrains-importing-a-codebase/install
Agent should check
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Copy prompt
Task: Use importing-a-codebase in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20importing-a-codebase%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jetbrains-importing-a-codebase/install
Install command: npx skills add JetBrains/thinkrail --skill importing-a-codebase
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/jetbrains-importing-a-codebase/install
LLM text format
/api/skills/jetbrains-importing-a-codebase/install?format=text
Find alternatives
/api/skills/search?q=importing-a-codebase&limit=3
Agent prompt
Use importing-a-codebase for this task. Review https://www.openagentskill.com/api/skills/jetbrains-importing-a-codebase/install, then install with: npx skills add JetBrains/thinkrail --skill importing-a-codebaseRegistry metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/jetbrains-importing-a-codebase
LLM text
/api/registry/manifest/jetbrains-importing-a-codebase?format=text
Install alias
/api/registry/install/jetbrains-importing-a-codebase
Recommend
/api/registry/recommend?task=Use%20importing-a-codebase%20in%20an%20agent%20workflow&limit=3
Agent fit
GitHub automation
Use-case tags
Platforms
Claude Code, Cursor
Audit report
Needs review · 79/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for GitHub automation
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
GitHub automation
Trust label
Prototype first
Install path
Command ready
Use when
- GitHub automation workflows
- Claude Code teams
- builders willing to evaluate younger projects
Evidence
- recent repository activity
- install command or GitHub repo available
- 63/100 quality profile
review first
- Low GitHub adoption signal
- No OpenAgentSkill engagement data yet
Implementation path
- 1Install it in a sandbox agent and run one GitHub automation task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Trust profile
Sandbox only
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
CHECK38 GitHub stars
Stars/forks activity
CHECK38 stars, 8 forks; issue activity unavailable in current metadata
Recent maintenance
PASSPushed today
License clarity
PASSApache-2.0
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Install command has no obvious high-risk pattern
- Outcome loop is ready but needs first real agent run
Review before install
- Financial research output is not financial advice; require human review before any live investment decision.
- Low GitHub adoption signal
- Quality score needs review
- GitHub adoption: 38 GitHub stars
- Stars/forks activity: 38 stars, 8 forks; issue activity unavailable in current metadata
- No real agent outcome reports yet
- Human review required before unattended installation
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Promising candidate for agent workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Use this skill in these scenarios
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Workflow fit
Add it to a complete workflow
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Compare before you install
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Overview
--- name: importing-a-codebase description: "Use when the repo holds real source code but no specs: the existing-codebase branch of setting-up-a-project, normally reached via that dispatcher, directly only when the situation is unmistakable. Not for empty workspaces (starting-a-new-project) or feature work in a specced project (brainstorming)." ---
# Importing a codebase
The workspace holds real code but no specs. **Reverse-engineer the spec graph the project should have had.** When the repo already carries real spec-like documents, build the graph around them, not parallel to them. Do as much as possible yourself, from the files; ask the user only where the code genuinely can't tell you and the answer changes a spec.
**Hold the writing-specs bar.** Read that concept skill before drafting — everything in this flow is inferred rather than confirmed, so its honesty rules (draft until the user reviews, unconfirmed marked inline) bind hardest here.
## 1. Read first, ask last
Survey before you ask a single question. Read, in roughly this order:
- **Agent files (mine these first — they state intent + conventions directly):** `AGENTS.md`, `CLAUDE.md`, `.cursor/rules/*`, `.cursorrules`, `.github/copilot-instructions.md`, `GEMINI.md`, `.windsurfrules`. - **Docs:** `README`, `docs/`, `CONTRIBUTING`, ADRs. - **Manifests & layout:** `package.json` / `pyproject.toml` / `go.mod` / `Cargo.toml`, workspace globs, `tree`-style structure, entry points, build/test scripts. - **Code:** entry points and the top of each candidate module — enough to see responsibilities and the dependency edges between them.
While you read, collect **adoption candidates**: durable, declarative documents that state the world as it is — architecture/design docs, ADRs / decision records, domain glossaries, protocol/contract docs. Never candidates (input only): READMEs, CONTRIBUTING, changelogs, roadmaps, TODOs, implementation plans (finished or planned), generated API docs.
Confirm with the spec tools (`spec_grep` / `spec_graph`) that there's no graph yet. If specs already exist, stop and hand back to the `setting-up-a-project` dispatcher — this flow is for un-specced repos.
## 2. Build a working model
From what you read, form a working model of what the project **is** and how it's **shaped** — held in the conversation, not written to a file (this flow declares no working files):
``` what: one-sentence purpose (the job the codebase does) domain: the space it's in stack: languages / frameworks / runtime modules: the real boundaries + the dependency edges between them (who imports whom) invariants: rules the code already enforces (layering, "X never imports Y", public surfaces) decisions: non-obvious choices visible in the code (and where the "why" is missing) ```
Agent files and READMEs usually hand you `what`, `invariants`, and `decisions` for free — prefer them over re-deriving from code.
## 3. Interview only the gaps
Ask **only** what the files can't answer and that would change a spec — typically: the primary job / who it's for, explicit non-goals, and the *why* behind a non-obvious decision. Batch them per the **asking-user-questions** concept skill; infer a concrete answer and let the user correct it rather than asking open-ended.
If adoption candidates exist, add one question to the same round: a multiSelect listing them (grouped when many — an `adr/` set is one option) — which should become spec-graph nodes? A contradiction between a candidate and the code found by now goes into the round too (confirm the correction). Skipped or declined → adopt none; candidates stay input, noted at hand-off.
If the files answered everything material, **skip the interview** and say so — don't manufacture questions (adoption candidates alone still make a round — the offer is never dropped as "no gaps"). A skipped/declined question is not a blocker: record the assumption inline in the spec, marked unconfirmed.
## 4. Draft the graph, top-down
Save with the spec tools as you go (`spec_create` per node, `edit` for prose). Order:
1. **`goal-and-requirements.md`** (`type: goal-and-requirements`) — the goal + scope. This is the graph root; the confirmed intent lives here. 2. **`architecture.md`** (`type: architecture-design`, `parent: <goal id>`) — topology, the module boundaries, the real dependency edges (a small DAG only if it carries real information), and the invariants the code enforces. 3. **One short `SPEC.md` per genuine module** (`type: module-design`, or `submodule-design` for a directory-level module inside a package; `parent:` its enclosing module or `architecture`). Each states its **responsibility** and its **boundary** (allowed deps / forbidden reaches).
**Adopted docs become nodes in place.** First, for each accepted candidate: read it carefully, then add spec frontmatter where the file lies (`id`, `type`, `title`, `status: draft`, `parent`; `depends-on`/`references` only where real) — content untouched. A slot an adopted doc fills is not drafted again: an adopted architecture doc *is* the `architecture-design` node, an adopted module design doc *is* that module's node, an ADR earns a node only while its decision is still in force. Build the rest of the graph around them, linked by id. One exception to "content untouched": where an adopted doc is unclear or has drifted from the code, correct that content as part of adoption and call the correction out (in the interview round when caught in time, at hand-off otherwise).
Wire `parent` to mirror the code hierarchy and `depends-on` only on edges the code actually shows. Keep each file **you draft** to the **writing-specs** bar — its granularity and say-it-once rules decide what counts as a module and where shared edges live. If a boundary is genuinely unclear, ask, or leave that spec `draft` with a one-line note — don't guess elaborately.
## 5. Validate & hand off
- Run `spec_validate`; fix dangling links, duplicate ids, parent cycles. - Tell the user the specs are drafted on this workspace's branch — **review them in Changes; nothing merges until they approve** — and summarize what you inferred vs. what they confirmed, which docs were adopted vs. left as input, and any drift corrections made. - Point at `brainstorming` for feature work from here on — **this workflow ends here**.
Technical details
- Version
- 1.0.0
- License
- Apache-2.0
- Last updated
- Aug 24, 2026
- Published
- Aug 24, 2026
Decision snapshot
Fallback candidate
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 84/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- Success rate
- —
- Recent failure
- —
- Outcomes
- 0
- Output quality
- —
- Failed
- 0
- Not relevant
- 0
- Installs
- 0
- Risk blocked
- 0
- Setup needed
- 0
- Production
- 0
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Add to agent workflow
Free and open source. Review the report before installing into production agents.
Growth loop
Share kit
Scenario-led draft for importing-a-codebase, ready for a manual X post.
importing-a-codebase: Use when the repo holds real source code but no specs: the existing-codebase branch of settin... 38 stars https://www.openagentskill.com/skills/jetbrains-importing-a-codebase?ref=x
Optional reply with install command
Listing + install path for importing-a-codebase: https://www.openagentskill.com/skills/jetbrains-importing-a-codebase?ref=x Install: npx skills add JetBrains/thinkrail --skill importing-a-codebase
Listing source
Registry indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- JetBrains
- Source
- JetBrains/thinkrail
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This Registry indexed listing is attributed to JetBrains but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/jetbrains-importing-a-codebase)
[](https://www.openagentskill.com/skills/jetbrains-importing-a-codebase)
[](https://www.openagentskill.com/skills/jetbrains-importing-a-codebase/audit)
[](https://www.openagentskill.com/skills/jetbrains-importing-a-codebase)Author
JetBrains
@jetbrains
Tags
Platform fit
Health signals
- GitHub stars
- 38
- Quality score
- 35/100
- Last GitHub push
- Aug 24, 2026
- Framework hints
- Unknown
- OpenAgentSkill views
- 0
- Install copies
- 0
- Outbound clicks
- 0
Community signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & safety
Sandbox only
- GitHub adoption38 GitHub starsCHECK
- Stars/forks activity38 stars, 8 forks; issue activity unavailable in current metadataCHECK
- Recent maintenancePushed todayPASS
- License clarityApache-2.0PASS
- README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
- Dependency/runtime risknetwork or browser surfacePASS
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